iris.clusterings

<p>Randomly generated clusterings for the <code>iris</code> dataset.</p>

datasets

The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.

David B Dahl

salso

Search Algorithms and Loss Functions for Bayesian Clustering

David B. Dahl

Devin J. Johnson

Peter Müller

Andrés Felipe Barrientos

Garritt Page

David Dunson

Alex Crichton

Brendan Zabarauskas

David Tolnay

Jim Turner

Josh Stone

R. Janis Goldschmidt

Sean McArthur

Stefan Lankes

The Cranelift Project Developers 

The CryptoCorrosion Contributors 

The Rand Project Developers 

The Rust Project Developers 

Ulrik Sverdrup "bluss"

bluss 

iris.clusterings function

<p>A 1000-by-150 matrix of 1000 randomly generated clusterings of the
150 observations in the <code>iris</code> dataset.</p>

Format

Clusterings of the Iris Data — iris.clusterings

Clusterings of the Iris Data

iris.clusterings: Clusterings of the Iris Data

Description

Usage

Arguments

Format

See Also